Final day at the @aiDotEngineer AI World's Fair in SF. We are at Booth U-G24, and our Chief Dancing Officer is reporting for duty. Come talk agents, evals, and models with the W&B team. https://t.co/J5grwPN4IQ

🆕 @Kimi_Moonshot's Kimi K2.7 Code is now generally available in GitHub Copilot. This is the first open-weight model offered as a selectable option in the Copilot model picker. 🎉 Early testing shows Kimi K2.7 is a lower-cost option with strong performance comparable to highly popular frontier models. Try it out now in @code and look out for it in more GitHub Copilot surfaces. 👇 https://t.co/ER2bGMNs7i

Fable 5 is a large step for Anthropic's vision capabilities and effectively ties with GPT-5.5 on HieroglyphBench, my benchmark which tests how well VLMs can transcribe ancient Egyptian hieroglyphs However, they're both still far behind the Gemini series, where 3.5 Flash has more than double the score

Vending machine https://t.co/j3wo4yCKn8

Built a working demo game (and saved me some money). Now its developing an original FPS concept and porting it to WebGL. https://t.co/zP3nTaDOdf

Introducing EBR-bench, our new benchmark to measure on-the-fly learning. AI repeatedly plays a challenging board game called Earthborne Rangers and tries to learn from its mistakes. So far: no signs of improvement. https://t.co/6R9wNHpnBu

@washingtonpost https://t.co/Nl8kYQJOIe

@aaronsibarium https://t.co/Nl8kYQJOIe

@bokuHaruyaHaru https://t.co/Nl8kYQJOIe

@b_diane_x https://t.co/Nl8kYQJOIe

@NeuroTechnoWtch https://t.co/Nl8kYQJOIe

Anthropic is paying $3,850 a week to people with no AI experience. No PhD required. No published papers. No prior research background. Just a strong technical mind and a genuine interest in making AI safe. This is the Anthropic Fellows Program. And it is one of the most underrated opportunities in technology right now. Here is exactly what it is. The Anthropic Fellows Program is designed to accelerate AI safety research and foster research talent providing funding and mentorship to promising technical talent regardless of previous experience. Fellows work for 4 months on empirical research questions aligned with Anthropic's overall research priorities, with the aim of producing public outputs like a paper. Four months. Full-time. Paid. Mentored by the researchers building the world's most advanced AI. And the results from the first cohort were not small. Fellows developed agents that identified $4.6 million in blockchain smart contract vulnerabilities and discovered two novel zero-day exploits, demonstrating that profitable autonomous exploitation is now technically feasible. A year prior, an Anthropic fellow developed a method for rapid response to new ASL3 jailbreaks, techniques that block entire classes of high-risk jailbreaks after observing only a handful of attacks. This work became a key component of Anthropic's ASL3 deployment safeguards. Other fellows published the subliminal learning paper, the research proving AI models transmit behavioral traits through unrelated data which landed in Nature. Others produced the agentic misalignment research showing frontier models resort to blackmail when facing replacement. Others open-sourced attribution graph tools that let researchers trace the internal thoughts of large language models. Over 80% of fellows produced papers. Over 40% subsequently joined Anthropic full-time. 80% published. 40% hired. From a program that does not require any prior AI safety experience to enter. Here is what the program looks like in practice. Anthropic mentors pitch their project ideas to fellows, who choose and shape their project in close collaboration with their mentors. You are not assigned busywork. You are not a research assistant. You own the project. You work alongside the people who built Claude, who designed its safety systems, who published the papers that define the field. The stipend is $3,850 USD per week, approximately $61,600 for the full 4 months with access to a compute budget of approximately $10,000 per fellow per month for running experiments. Here is what the 2026 program covers. Research areas include scalable oversight, adversarial robustness and AI control, model organisms, mechanistic interpretability, AI security, model welfare, economics and policy, and reinforcement learning. Something for every technical background. Not just ML engineers. Successful fellows have come from physics, mathematics, computer science, and cybersecurity. You do not need a PhD, prior ML experience, or published papers. The one requirement: work authorization in the US, UK, or Canada. Anthropic does not sponsor visas for fellows. Here is the timeline you need to know. The next cohort begins July 20, 2026. Applications are reviewed on a rolling basis — earlier applications get more consideration. The process includes an initial application and reference check, technical assessments, interviews, and a research discussion. Applicants are encouraged to apply even if they do not meet every listed qualification. The program values potential, motivation, and research curiosity over rigid credential requirements. This is the rarest kind of opportunity in technology. A company at the frontier of AI, one valued at over $900 billion offering outsiders direct access to its research infrastructure, its mentors, and its most important open problems. Paying them generously to do it. And then hiring 40% of them afterward. Most people who want to work on AI safety spend years trying to publish papers, get into the right PhD program, and find a way in. The Fellows Program is the door they did not know existed. It is open right now.

@DavidSacks https://t.co/Nl8kYQJOIe

@shmidtqq https://t.co/Nl8kYQJOIe

Today we are rolling out Dreamina Seedance 2.0 4K in the U.S. Dropping in all relevant CapCut features: - App: AI Lab, AI Generator, AI Video - Web: Video Studio - Desktop: AI Video, Edit Pilot We're excited to see what you'll create with it. Have fun exploring! RT+Comment in 4hr to get extra 200 credit in your DM

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this @dair_ai) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!

Oh and tomorrow (today? fck I gotta go sleep) @thursdai_pod has an INSANE lineup. https://t.co/fIB2DHFfDr Tune in on YT and on here on X: 9am - @alexocheema @0xSero to talk local AI (@exolabs) 9:30 - @dkundel from @OpenAI 10:00 - @Stefania_druga from @SakanaAILabs 10:15 - @_philschmid from @GoogleAI and to close the show, the one and only @swyx (+ a surprise guest)

@altryne Am I counting as live studio audience @altryne ? https://t.co/QZNIVqfN0B

@altryne Am I counting as live studio audience @altryne ? https://t.co/QZNIVqfN0B

@altryne @thursdai_pod @aiDotEngineer @_philschmid @GoogleAI Iconic @thursdai_pod edition https://t.co/si4Od9Ts5E

@altryne @thursdai_pod @aiDotEngineer @_philschmid @GoogleAI Iconic @thursdai_pod edition https://t.co/si4Od9Ts5E

The future of the firm is a learning loop in which human capital and token capital compound. With our new Frontier Co., our ambition is to help every enterprise build its own AI capability, and to help create a frontier ecosystem where every organization can turn its knowledge, workflows, and judgment into its own AI systems that continuously improve. https://t.co/mvYhkRFyqa

@john_my07 I ran the same prompt through @imagine Agent mode and the results were pretty amazing! https://t.co/blif4G0bmD

LiteParse runs anywhere, including inside agent runtimes. To prove it, we built an email processing assistant using @flueai (the agentic framework by @astrodotbuild), @resend webhooks, and @tursodatabase for persistence📩 Here's how it works: → Send an email → The agent reads its corpus, fetches attachments, and parses any PDFs with LiteParse → It summarizes the message and drafts a reply for you This integration is an example of how parsing tools shouldn't just handle files, they should deepen what agents understand about them, augmenting their context📂 👩‍💻 Code here: https://t.co/LSTIwKLuy4

Setting up with @NVIDIAAI Local AI Summit, Room 2009 at @aiDotEngineer Demos: - Running GLM 5.2 on DGX Station - Running Nemotron 3 Ultra on 4 x DGX Spark https://t.co/NS95kcXEv4

OpenAI's reported discussions with the U.S. government caught my attention. If they move forward, it would signal just how closely the future of frontier AI is becoming intertwined with national strategy. That's a very different landscape from where the industry started just a few years ago.

https://t.co/6G0NAesDBT

// AutoMem // I quite like this idea of metamemory. (bookmark it) This new research from Stanford treats agent's memory management as a trainable skill instead of a fixed module. The model decides what to encode, when to retrieve, and how to organize its own notes, with file-system operations promoted to first-class actions right alongside task actions. AutoMem automates this on two loops. A strong LLM reviews full trajectories and rewrites the memory structure (prompts, schemas, action vocabulary). Then the agent's own good memory decisions across episodes become training signal to sharpen its proficiency. Optimizing memory alone, without touching task-action behavior, lifts the base agent 2x to 4x on Crafter, MiniHack, and NetHack. That is enough to make a 32B open model competitive with Claude Opus 4.5 and Gemini 3.1 Pro Thinking. For long-horizon agents, memory is a high-leverage objective you can train for on its own. Paper: https://t.co/bBjjK0VEOS Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

BREAKING: Japan is testing Starlink-powered fire hydrant signs as emergency communication hubs. • Japan has approximately 120,000 fire hydrant signs installed nationwide. • The technical demonstration was conducted by Shokasen Hyoshiki Co., Ltd., a company that manages fire hydrant signs across Japan. • The test explored mounting Starlink equipment on a fire hydrant sign to provide satellite internet connectivity. • The goal is to transform existing fire hydrant signs into temporary Wi-Fi and emergency communication hubs during disasters. • Residents could use them to access emergency information and stay connected if cellular and terrestrial internet networks go down. • The project is currently an early-stage technical verification and has not yet been deployed commercially or nationwide. This demonstration shows how @elonmusk's Starlink could help keep communities connected when traditional networks are unavailable.

Warren identified the deeper problem almost a century before Ludwig von Mises and Friedrich Hayek made it famous in academic economics. When everyone owns everything, no one owns anything. When labor earns the same reward regardless of effort, effort disappears. When prices vanish, no one knows what anything is worth.